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www.databrew.cc
2020-04-01
## Load libraries
library(covid19)
library(ggplot2)
library(lubridate)
library(dplyr)
library(ggplot2)
library(sp)
library(raster)
library(viridis)
library(ggthemes)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(RColorBrewer)
library(readr)
options(scipen = '999')roll_curve <- function(data,
n = 4,
deaths = FALSE,
scales = 'fixed',
nrow = NULL,
ncol = NULL,
pop = FALSE){
# Create the n day rolling avg
ma <- function(x, n = 5){
if(length(x) >= n){
stats::filter(x, rep(1 / n, n), sides = 1)
} else {
new_n <- length(x)
stats::filter(x, rep(1 / new_n, new_n), sides = 1)
}
}
pd <- data
if(deaths){
pd$var <- pd$deaths_non_cum
} else {
pd$var <- pd$cases_non_cum
}
if('ccaa' %in% names(pd)){
pd$geo <- pd$ccaa
} else {
pd$geo <- pd$iso
}
if(pop){
# try to get population
if(any(pd$geo %in% unique(esp_df$ccaa))){
right <- esp_pop
right_var <- 'ccaa'
} else {
right <- world_pop
right_var <- 'iso'
}
pd <- pd %>% left_join(right %>% dplyr::select(all_of(right_var), pop),
by = c('geo' = right_var)) %>%
mutate(var = var / pop * 100000)
}
pd <- pd %>%
arrange(date) %>%
group_by(geo) %>%
mutate(with_lag = ma(var, n = n))
ggplot() +
geom_bar(data = pd,
aes(x = date,
y = var),
stat = 'identity',
fill = 'grey',
alpha = 0.8) +
geom_area(data = pd,
aes(x = date,
y = with_lag),
color = 'red',
fill = 'red',
alpha = 0.6) +
facet_wrap(~geo, scales = scales, nrow = nrow, ncol = ncol) +
theme_simple() +
labs(x = '',
y = ifelse(deaths, 'Deaths', 'Cases'),
title = paste0('Daily (non-cumulative) ', ifelse(deaths, 'deaths', 'cases'),
ifelse(pop, ' (per 100,000)', '')),
subtitle = paste0('Data as of ', max(data$date),
'.\nRed line = ', n, ' day rolling average. Grey bar = day-specific value.')) +
theme(axis.text.x = element_text(size = 7,
angle = 90,
hjust = 0.5, vjust = 1)) #+
# scale_x_date(name ='',
# breaks = sort(unique(pd$date)),
# labels = format(sort(unique(pd$date)), '%b %d'))
# scale_y_log10()
}Catalunya, corbes epidèmiques
Corba epidèmica. CASOS. -Barras en gris: casos diaris -Vermell: tendència (mitjana dels 4 dies anteriors)
this_ccaa <- 'Cataluña'
plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
roll_curve(plot_data, scales = 'fixed') + theme(strip.text = element_text(size = 30))
ggsave('~/Desktop/totesmou/00a_catalunya_corba_epidemica_casos.png',
height = 5.6,
width = 9.6)Corba epidèmica. MORTS -Barras en gris: casos diaris -Vermell: tendència (mitjana dels 4 dies anteriors)
plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
roll_curve(plot_data, deaths = T, scales = 'fixed') + theme(strip.text = element_text(size = 30))
ggsave('~/Desktop/totesmou/00b_catalunya_corba_epidemica_morts.png',
height = 5.6,
width = 9.6)Espanya, corbes epidèmiques
Corba epidèmica. CASOS. -Barras en gris: casos diaris -Vermell: tendència (mitjana dels 4 dies anteriors)
the_country = 'Spain'
plot_data <- df_country %>% filter(country %in% the_country) %>% mutate(geo = country)
roll_curve(plot_data, pop = F)
ggsave('~/Desktop/totesmou/00c_espanya_epidemica_casos.png',
height = 5.6,
width = 9.6)Corba epidèmica. MORTS -Barras en gris: casos diaris -Vermell: tendència (mitjana dels 4 dies anteriors)
plot_data <- df_country %>% filter(country %in% the_country) %>% mutate(geo = country)
roll_curve(plot_data, pop = F, deaths = T)
ggsave('~/Desktop/totesmou/00d_espanya_corba_epidemica_morts.png',
height = 5.6,
width = 9.6)CCAA, corbes epidèmiques
Corba epidèmica. CASOS. -Barras en gris: casos diaris -Vermell: tendència (mitjana dels 4 dies anteriors)
plot_data <- esp_df %>% mutate(geo = ccaa)
roll_curve(plot_data, scales = 'fixed', pop = T) + theme(strip.text = element_text(size = 15))
ggsave('~/Desktop/totesmou/00e_ccaa_corba_epidemica_casos.png',
height = 5.6,
width = 9.6)Corba epidèmica. MORTS -Barras en gris: casos diaris -Vermell: tendència (mitjana dels 4 dies anteriors)
plot_data <- esp_df %>% mutate(geo = ccaa)
roll_curve(plot_data, scales = 'fixed', pop = T, deaths = T) + theme(strip.text = element_text(size = 15))
ggsave('~/Desktop/totesmou/00f_ccaa_corba_epidemica_morts.png',
height = 5.6,
width = 9.6)Comparacions amb Ità lia
Comparació Ità lia vs. Espanya, casos. Aix-X: data real
dir.create('~/Desktop/totesmou')
plot_day_zero(countries = c('Spain', 'Italy'),
point_size = 2, calendar = T)
ggsave('~/Desktop/totesmou/1_italy_vs_spain.png',
height = 5.6,
width = 9.6)Comparació Ità lia vs. Espanya, casos. Aix-X: data relativa (des del principi del brot)
plot_day_zero(countries = c('Spain', 'Italy'),
point_size = 2, calendar = F)
ggsave('~/Desktop/totesmou/2_italy_vs_spain_temps_ajustat.png',
height = 5.6,
width = 9.6)Comparació Ità lia vs. Espanya, morts Aix-X: data real
plot_day_zero(countries = c('Spain', 'Italy'),
point_size = 2, calendar = T, deaths = T, day0 = 10)
ggsave('~/Desktop/totesmou/3_italy_vs_spain_morts.png',
height = 5.6,
width = 9.6)Comparació Ità lia vs. Espanya, morts Aix-X: data relativa (des del principi del brot)
plot_day_zero(countries = c('Spain', 'Italy'),
point_size = 2, calendar = F, deaths = T, day0 = 10)
ggsave('~/Desktop/totesmou/4_italy_vs_spain_morts_temps_ajustat.png',
height = 5.6,
width = 9.6)Comparació Ità lia vs. Espanya, morts Aix-X: data relativa (des del principi del brot), ajustat per població
plot_day_zero(countries = c('Spain', 'Italy'),
point_size = 2, calendar = F, deaths = T, day0 = 10, pop = T)
ggsave('~/Desktop/totesmou/5_italy_vs_spain_morts_temps_ajustat_poblacio.png',
height = 5.6,
width = 9.6)Comparació Ità lia vs. Espanya, morts Aix-X: data relativa (des del principi del brot), ajustat per població, morts dià ries amb tendència darrers 7 dies
plot_day_zero(countries = c('Spain', 'Italy'),
point_size = 2, calendar = F, deaths = T, day0 = 1, pop = T, roll = 7, roll_fun = 'mean')
ggsave('~/Desktop/totesmou/6_italy_vs_spain_morts_diarias_rolling_7_day_avg_temps_ajustat_poblacio.png',
height = 5.6,
width = 9.6)Regions d’Ità lia vs Espanya. Madrid / Cat + Lombardia / ER. Ajustat per població. CASOS.
plot_day_zero(countries = c('Spain', 'Italy'),
districts = c('Cataluña', 'Madrid',
'Lombardia', 'Emilia-Romagna'),
by_district = T,
day0 = 10,
pop = T)
ggsave('~/Desktop/totesmou/7_ccaa_casos_ajustat_poblacio.png',
height = 5.6,
width = 9.6)Regions d’Ità lia vs Espanya. Madrid / Cat + Lombardia / ER. Ajustat per població. MORTS
plot_day_zero(countries = c('Spain', 'Italy'),
districts = c('Cataluña', 'Madrid',
'Lombardia', 'Emilia-Romagna'),
by_district = T,
deaths = T,
day0 = 1,
pop = T)
ggsave('~/Desktop/totesmou/8_ccaa_morts_ajustat_poblacio.png',
height = 5.6,
width = 9.6)Regions d’Ità lia vs Espanya. Madrid / Cat + Lombardia / ER. Ajustat per població. MORTS DIARIES
plot_day_zero(countries = c('Spain', 'Italy'),
districts = c('Cataluña', 'Madrid',
'Lombardia', 'Emilia-Romagna'),
by_district = T,
deaths = T,
roll = 7,
roll_fun = 'mean',
day0 = 1,
pop = T)
ggsave('~/Desktop/totesmou/9_ccaa_morts_ajustat_poblacio.png',
height = 5.6,
width = 9.6)Regions d’Ità lia vs Espanya vs New York (USA). Morts per població
plot_day_zero(countries = c('Spain', 'Italy', 'US'),
districts = c('Cataluña', 'Madrid',
'New York',
'Lombardia', 'Emilia-Romagna'),
by_district = T,
deaths = T,
# roll = 7,
# roll_fun = 'mean',
day0 = 1,
pop = T)
ggsave('~/Desktop/totesmou/10_usa_comparison.png',
height = 5.6,
width = 9.6)Regions d’Ità lia vs Espanya vs New York (USA). Morts sense ajustar per població
plot_day_zero(countries = c('Spain', 'Italy', 'US'),
districts = c('Cataluña', 'Madrid',
'New York',
'Lombardia', 'Emilia-Romagna'),
by_district = T,
deaths = T,
# roll = 7,
roll_fun = 'mean',
day0 = 1,
pop = F)
ggsave('~/Desktop/totesmou/11_usa_comparison_no_ajust_pop.png',
height = 5.6,
width = 9.6)Regions d’Ità lia vs Espanya
plot_day_zero(color_var = 'iso', by_district = T,
deaths = T,
day0 = 1,
alpha = 0.6,
point_alpha = 0.6,
countries = c('Spain', 'Italy'),
pop = T)
ggsave('~/Desktop/totesmou/12_ita_us_ccaa_comparison.png',
height = 5.6,
width = 9.6)Madrid vs resta estat i Lombardia vs resta estat
place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
# ifelse(x == 'Cataluña', 'Cataluña',
'Altres CCAA')
# )
}
place_transform_ita <- function(x){
ifelse(x == 'Lombardia', 'Lombardia',
# ifelse(x == 'Emilia Romagna', 'Emilia Romagna',
'Altres regions italianes')
# )
}
pd <- esp_df %>% mutate(country = 'Espanya') %>%
mutate(ccaa = place_transform(ccaa)) %>%
bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
country = 'Ità lia')) %>%
group_by(country, ccaa, date) %>%
summarise(cases = sum(cases),
uci = sum(uci),
deaths = sum(deaths),
cases_non_cum = sum(cases_non_cum),
deaths_non_cum = sum(deaths_non_cum),
uci_non_cum = sum(uci_non_cum)) %>%
left_join(esp_pop %>%
mutate(ccaa = place_transform(ccaa)) %>%
bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
group_by(ccaa) %>%
summarise(pop = sum(pop))) %>%
mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
group_by(country, date) %>%
mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
levels = c('Madrid',
# 'Cataluña',
'Altres CCAA',
'Lombardia',
# 'Emilia Romagna',
'Altres regions italianes'))
cols <- c(
rev(brewer.pal(n = 3, 'Reds'))[1:2],
rev(brewer.pal(n = 3, 'Blues'))[1:2]
)
label_data <- pd %>%
filter(country %in% c('Ità lia', 'Espanya')) %>%
group_by(country) %>%
filter(date == max(date)) %>%
mutate(label = gsub('\nitalianas', '', gsub(' ', '\n', ccaa))) %>%
mutate(x = date - 2,
y = p_deaths_country +
ifelse(p_deaths_country > 50, 10, -9))
ggplot(data = pd %>% group_by(country) %>% mutate(start_day = dplyr::first(date[deaths >=50])) %>% filter(date >= start_day),
aes(x = date,
y = p_deaths_country,
color = ccaa,
group = ccaa)) +
# geom_bar(stat = 'identity',
# position = position_dodge(width = 0.99)) +
scale_fill_manual(name = '', values = cols) +
scale_color_manual(name = '', values = cols) +
geom_line(size = 2,
aes(color = ccaa)) +
geom_point(size = 3,
aes(color = ccaa)) +
facet_wrap(~country, scales = 'free_x') +
# xlim(as.Date('2020-03-09'),
# Sys.Date()-1) +
theme_simple() +
geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
# geom_line(lty = 2, alpha = 0.6) +
labs(x = 'Data',
y = 'Percentatge de morts',
title = 'Percentatge de morts de la regió més afectada vs resta del pais',
subtitle = 'A partir del primer dÃa a cada paÃs amb 50 o més morts') +
theme(legend.position = 'top',
strip.text = element_text(size = 30),
legend.text = element_text(size = 10)) +
guides(color = guide_legend(nrow = 2,
keywidth = 5)) +
geom_text(data = label_data,
aes(x = x-1,
y = y,
label = label,
color = ccaa),
size = 7,
show.legend = FALSE)
ggsave('~/Desktop/totesmou/13_madrid_lombardia_percent.png',
height = 8,
width = 12)Map of portugal, france, spain
pd <- por_df %>% mutate(country = 'Portugal') %>%
bind_rows(esp_df %>% mutate(country = 'Spain')) %>%
bind_rows(fra_df %>% mutate(country = 'France')) %>%
bind_rows(ita %>% mutate(country = 'Italy')) %>%
bind_rows(
df %>% filter(country == 'Andorra') %>%
mutate(ccaa = 'Andorra')
)
keep_date = pd %>% group_by(country) %>% summarise(max_date= max(date)) %>% summarise(x = min(max_date)) %>% .$x
pd <- pd %>%
group_by(ccaa) %>%
filter(date == keep_date) %>%
# filter(date == '2020-03-27') %>%
ungroup %>%
dplyr::select(date, ccaa, deaths, deaths_non_cum,
cases, cases_non_cum) %>%
left_join(regions_pop %>%
bind_rows(
world_pop %>% filter(country == 'Andorra') %>% dplyr::mutate(ccaa = country) %>%
dplyr::select(-region, -sub_region)
)) %>%
mutate(cases_per_million = cases / pop * 1000000,
deaths_per_million = deaths / pop * 1000000) %>%
mutate(cases_per_million_non_cum = cases_non_cum / pop * 1000000,
deaths_per_million_non_cum = deaths_non_cum / pop * 1000000)
map_esp1 <- map_esp
map_esp1@data <- map_esp1@data %>% dplyr::select(ccaa)
map_fra1 <- map_fra
map_fra1@data <- map_fra1@data %>% dplyr::select(ccaa = NAME_1)
map_por1 <- map_por
map_por1@data <- map_por1@data %>% dplyr::select(ccaa = CCDR)
map_ita1 <- map_ita
map_ita1@data <- map_ita1@data %>% dplyr::select(ccaa)
map_and1 <- map_and
map_and1@data <- map_and1@data %>% dplyr::select(ccaa = NAME_0)
map <-
rbind(map_esp1,
map_por1,
map_fra1,
map_ita1,
map_and1)
# Remove areas not of interest
centroids <- coordinates(map)
centroids <- data.frame(centroids)
names(centroids) <- c('x', 'y')
centroids$ccaa <- map@data$ccaa
centroids <- left_join(centroids, pd)
# map <- map_sp <- map[centroids$y >35 & centroids$x > -10 &
# centroids$x < 8 & (centroids$y < 43 | map@data$ccaa %in% c('Occitanie', 'Nouvelle-Aquitaine') |
# map@data$ccaa %in% esp_df$ccaa),]
map_sp <- map <- map[centroids$x > -10 & centroids$y <47,]
# map_sp <- map <- map[centroids$x > -10,]
# fortify
map <- fortify(map, region = 'ccaa')
# join data
map$ccaa <- map$id
map <- left_join(map, pd)
var <- 'deaths_per_million'
map$var <- as.numeric(unlist(map[,var]))
centroids <- centroids[,c('ccaa', 'x', 'y', var)]
centroids <- centroids %>%
filter(ccaa %in% map_sp@data$ccaa)
# cols <- rev(RColorBrewer::brewer.pal(n = 9, name = 'Spectral'))
# cols <- c('#A16928','#bd925a','#d6bd8d','#edeac2','#b5c8b8','#79a7ac','#2887a1')
# cols <- c('#009392','#39b185','#9ccb86','#e9e29c','#eeb479','#e88471','#cf597e')
# cols <- c('#008080','#70a494','#b4c8a8','#f6edbd','#edbb8a','#de8a5a','#ca562c')
cols <- rev(colorRampPalette(c('darkred', 'red', 'darkorange', 'orange', 'yellow', 'lightblue'))(10))
g <- ggplot(data = map,
aes(x = long,
y = lat,
group = group)) +
geom_polygon(aes(fill = var),
lwd = 0.3,
color = 'darkgrey',
size = 0.6) +
scale_fill_gradientn(name = '',
colours = cols) +
# scale_fill_() +
ggthemes::theme_map() +
theme(legend.position = 'bottom',
plot.title = element_text(size = 16)) +
guides(fill = guide_colorbar(direction= 'horizontal',
barwidth = 50,
barheight = 1,
label.position = 'bottom')) +
labs(title = 'Cumulative COVID-19 deaths per million population, western Mediterranean',
subtitle = paste0('Data as of ', format(max(pd$date), '%B %d, %Y'))) +
geom_text(data = centroids,
aes(x = x,
y = y,
group = NA,
label = paste0(ccaa, '\n',
round(deaths_per_million, digits = 2))),
alpha = 0.8,
size = 3)
g
ggsave('~/Desktop/totesmou/15_mapa_mediterranea.png',
height = 8,
width = 12)